Exploration into Single Image Super-Resolution via Self Similarity by Sparse Representation

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概要

• 論文の詳細を見る

• A novel method for single image super resolution without any training samples is presented in the paper. By sparse representation, the method attempts to recover at each pixel its best possible resolution increase based on the self similarity of the image patches across different scale and rotation transforms. The experiments indicate that the proposed method can produce robust and competitive results.

• （社）電子情報通信学会の論文
• 2010-11-01

著者

• GUO Lv

  IPRAI, Shanghai Jiao Tong University

• LI Yin

  IPRAI, Shanghai Jiao Tong University

• YANG Jie

  IPRAI, Shanghai Jiao Tong University

• LU Li

  IPRAI, Shanghai Jiao Tong University

• Yang Jie

  Iprai Shanghai Jiao Tong University

• Lu Li

  Iprai Shanghai Jiao Tong University

• Li Yin

  Iprai Shanghai Jiao Tong University

• Guo Lv

  Iprai Shanghai Jiao Tong University

関連論文

• Hole-Filling by Rank Sparsity Tensor Decomposition for Medical Imaging
• Exploration into Single Image Super-Resolution via Self Similarity by Sparse Representation

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